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Found 13 papers, 6 papers with code
Lazy Diffusion Transformer for Interactive Image Editing
We demonstrate that our approach is competitive with state-of-the-art inpainting methods in terms of quality and fidelity while providing a 10x speedup for typical user interactions, where the editing mask represents 10% of the image.
Saliency-Aware Regularized Graph Neural Network
In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone.
Perception Reinforcement Using Auxiliary Learning Feature Fusion: A Modified Yolov8 for Head Detection
Head detection provides distribution information of pedestrian, which is crucial for scene statistical analysis, traffic management, and risk assessment and early warning.
Edge-aware Plug-and-play Scheme for Semantic Segmentation
Therefore, we propose an abstract and universal edge supervision method called Edge-aware Plug-and-play Scheme (EPS), which can be easily and quickly applied to any semantic segmentation models.
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis
In the forward stage, we take advantage of LTH with rewinding weights to progressively shrink the SR model and the pruning-out masks that form nested sets.
Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks
As a fundamental computer vision task, crowd counting plays an important role in public safety.
Harmonizing output imbalance for defect segmentation on extremely-imbalanced photovoltaic module cells images
This extreme imbalance makes it difficult to segment the THC of PV module cells, which is also a challenge for semantic segmentation.
Variational Distillation for Multi-View Learning
Information Bottleneck (IB) based multi-view learning provides an information theoretic principle for seeking shared information contained in heterogeneous data descriptions.
Third Time's the Charm? Image and Video Editing with StyleGAN3
In particular, we demonstrate that while StyleGAN3 can be trained on unaligned data, one can still use aligned data for training, without hindering the ability to generate unaligned imagery.
StyleAlign: Analysis and Applications of Aligned StyleGAN Models
Several works already utilize some basic properties of aligned StyleGAN models to perform image-to-image translation.
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images.
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works.
Maximum Correntropy Unscented Filter
To improve the robustness of the UKF against impulsive noises, a new filter for nonlinear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF).
